{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2b3e478b-5399-49b6-8a1b-3de120ddec6d",
   "metadata": {},
   "source": [
    "This notebook reproduces results for section 4.2 of the manuscript, including table 4 as well as the numbers in section 4.2.3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3cfa7f6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.metrics import accuracy_score, f1_score, matthews_corrcoef\n",
    "from transformers import pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "26b9ed11-7cf9-48e6-94d0-41eb256a026b",
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('./trump_test_data.csv')\n",
    "test['target'] = 'Donald Trump'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0fdb0596",
   "metadata": {},
   "outputs": [],
   "source": [
    "def analyze_results(df, true, preds, how = 'target'):\n",
    "    \"\"\"\n",
    "    Function to return preformance metrics for classification results by either target, dataset, or target and dataset.\n",
    "    \n",
    "    df: A dataframe containing the results\n",
    "    \n",
    "    true: string. The name of the column containing the true labels\n",
    "    \n",
    "    preds: string. The name of the column containing the predicted labels.\n",
    "    \n",
    "    how: Return results by target, dataset, or target and dataset\n",
    "    \"\"\"\n",
    "    if how == 'target':\n",
    "        # create a dictionary that will be converted to the results table.\n",
    "        table = {'Target': list(set(df['target'])), 'MCC':[], 'F1':[], 'Accuracy':[], 'n':[]}\n",
    "        # for each target in the dataset compute performance metrics and append to the dictionary\n",
    "        for target in set(df['target']):\n",
    "            table['MCC'].append(matthews_corrcoef(df.loc[df['target'] == target, true], df.loc[df['target'] == target, preds]))\n",
    "            table['F1'].append(f1_score(df.loc[df['target'] == target, true], df.loc[df['target'] == target, preds], average = 'binary'))\n",
    "            table['Accuracy'].append(accuracy_score(df.loc[df['target'] == target, true], df.loc[df['target'] == target, preds]))\n",
    "            table['n'].append(len(df.loc[df['target'] == target]))\n",
    "        # convert the dictionary to a table and print\n",
    "        print(pd.DataFrame.from_dict(table))\n",
    "    \n",
    "    elif how == 'dataset':\n",
    "        table = {'Dataset': list(set(df['dataset'])), 'MCC':[], 'F1':[], 'Accuracy':[], 'n':[]}\n",
    "        for ds in set(df['dataset']):\n",
    "            table['MCC'].append(matthews_corrcoef(df.loc[df['dataset'] == ds, true], df.loc[df['dataset'] == ds, preds]))\n",
    "            table['F1'].append(f1_score(df.loc[df['dataset'] == ds, true], df.loc[df['dataset'] == ds, preds], average = 'binary'))\n",
    "            table['Accuracy'].append(accuracy_score(df.loc[df['dataset'] == ds, true], df.loc[df['dataset'] == ds, preds]))\n",
    "            table['n'].append(len(df.loc[df['dataset'] == ds]))\n",
    "        print(pd.DataFrame.from_dict(table))\n",
    "        \n",
    "    elif how == 'target-dataset':\n",
    "        table = {'Dataset': list(set(df['target_dataset'])), 'MCC':[], 'F1':[], 'Accuracy':[], 'n':[]}\n",
    "        for tds in set(df['target_dataset']):\n",
    "            table['MCC'].append(matthews_corrcoef(df.loc[df['target_dataset'] == tds, true], df.loc[df['target_dataset'] == tds, preds]))\n",
    "            table['F1'].append(f1_score(df.loc[df['target_dataset'] == tds, true], df.loc[df['target_dataset'] == tds, preds], average = 'binary'))\n",
    "            table['Accuracy'].append(accuracy_score(df.loc[df['target_dataset'] == tds, true], df.loc[df['target_dataset'] == tds, preds]))\n",
    "            table['n'].append(len(df.loc[df['target_dataset'] == tds]))\n",
    "        print(pd.DataFrame.from_dict(table))\n",
    "\n",
    "def zsclassify(classifier, df, colname, hypotheses, hypothesis_template = 'This example is {}.', multi_label = False):\n",
    "    \"\"\"\n",
    "    classifier: The zero-shot classifier object\n",
    "    \n",
    "    df: Data frame containining targets and text samples.\n",
    "    \n",
    "    colname: The name of the column that will contain the results.\n",
    "    \n",
    "    hypotheses: A dictionary of targets and the associated classification labels.\n",
    "    \"\"\"\n",
    "    # initialize the column\n",
    "    df[colname] = 0\n",
    "    \n",
    "    for target in set(df['target']):\n",
    "        samples = df.loc[df['target'] == target, 'text']\n",
    "        # classify the documents, returns a dictionary for each text sample\n",
    "        print('Classifying ' + target + '...')\n",
    "        res = classifier(list(samples), hypotheses[target], hypothesis_template = hypothesis_template, multi_label = multi_label)\n",
    "        # pull labels from each dictionary and pass them to a list\n",
    "        #res = [label['labels'][0] for label in res]\n",
    "        \n",
    "        if len(hypotheses[target]) > 1:\n",
    "            res = [label['labels'][0] for label in res]\n",
    "        # if there is a single hypothesis, return the entailment score\n",
    "        elif len(hypotheses[target]) == 1:\n",
    "            res = [score['scores'][0] for score in res]\n",
    "        \n",
    "        # add results to the column\n",
    "        df.loc[df['target'] == target, colname] = res\n",
    "        # recode results from strings to numeric\n",
    "        #df.replace({hypotheses[target][0]: 1, hypotheses[target][1]: -1, hypotheses[target][2]: 0}, inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "810ec8ea",
   "metadata": {},
   "source": [
    "# NLI: Model Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "45fba165",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define a dictionary of targets and hypotheses\n",
    "g_dict = {'Donald Trump': ['supports']}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ce34cd2",
   "metadata": {},
   "source": [
    "### Large"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "200291d9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 6.33 s\n",
      "Wall time: 5.33 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# instatiate the pipeline\n",
    "model = 'MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli'\n",
    "classifier = pipeline(\"zero-shot-classification\", model=model, device = 0, batch_size = 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "781d35c5-bc53-48fe-b4c5-9a36711b5a6e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying Donald Trump...\n",
      "         Target       MCC        F1  Accuracy     n\n",
      "0  Donald Trump  0.653871  0.821668  0.825679  2134\n",
      "        Dataset       MCC        F1  Accuracy     n\n",
      "0  Kawintiranon  0.726106  0.820896  0.873684   380\n",
      "1          mtsd  0.567408  0.793722  0.768844   398\n",
      "2       Titling  0.473111  0.604167  0.763975   161\n",
      "3   newsletters  0.676045  0.856115  0.840000  1000\n",
      "4     constance  0.604006  0.680851  0.846939    98\n",
      "5       semeval  0.591036  0.837607  0.804124    97\n",
      "CPU times: total: 13.6 s\n",
      "Wall time: 13.9 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_19336\\2599676197.py:71: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. 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      "  df.loc[df['target'] == target, colname] = res\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "zsclassify(classifier, df = test, colname = 'gL', hypotheses = g_dict, hypothesis_template = 'The author of this text {} Trump.')\n",
    "test['gL'] = [1 if num >= .5 else 0 for num in test['gL']]\n",
    "\n",
    "# results by target\n",
    "analyze_results(test, 'adjudicated_sup', 'gL', 'target')\n",
    "\n",
    "# results by dataset\n",
    "analyze_results(test, 'adjudicated_sup', 'gL', 'dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f6367b4",
   "metadata": {},
   "source": [
    "### Base"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d1a2110a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# instatiate the pipeline\n",
    "model = 'MoritzLaurer/DeBERTa-v3-base-mnli-fever-anli'\n",
    "classifier = pipeline(\"zero-shot-classification\", model=model, device = 0, batch_size = 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cd416f59-5b5e-4fcb-9ab6-923ff2029293",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. Default to no truncation.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying Donald Trump...\n",
      "         Target       MCC        F1  Accuracy     n\n",
      "0  Donald Trump  0.395936  0.687045  0.697751  2134\n",
      "        Dataset       MCC        F1  Accuracy     n\n",
      "0  Kawintiranon  0.522800  0.711974  0.765789   380\n",
      "1          mtsd  0.378137  0.688442  0.688442   398\n",
      "2       Titling  0.400248  0.548673  0.683230   161\n",
      "3   newsletters  0.372403  0.703774  0.686000  1000\n",
      "4     constance  0.150937  0.366197  0.540816    98\n",
      "5       semeval  0.544189  0.800000  0.773196    97\n",
      "CPU times: total: 9 s\n",
      "Wall time: 9.09 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_19336\\2599676197.py:71: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. 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      "  df.loc[df['target'] == target, colname] = res\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "zsclassify(classifier, df = test, colname = 'gB', hypotheses = g_dict, hypothesis_template = 'The author of this text {} Trump.')\n",
    "test['gB'] = [1 if num >= .5 else 0 for num in test['gB']]\n",
    "\n",
    "analyze_results(test, 'adjudicated_sup', 'gB', 'target')\n",
    "\n",
    "analyze_results(test, 'adjudicated_sup', 'gB', 'dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "526ec2a6",
   "metadata": {},
   "source": [
    "### Small"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "16c2acdc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\.conda\\envs\\sandbox\\Lib\\site-packages\\transformers\\convert_slow_tokenizer.py:473: UserWarning: The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option which is not implemented in the fast tokenizers. In practice this means that the fast version of the tokenizer can produce unknown tokens whereas the sentencepiece version would have converted these unknown tokens into a sequence of byte tokens matching the original piece of text.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 4.08 s\n",
      "Wall time: 3.05 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "model = 'MoritzLaurer/DeBERTa-v3-small-mnli-fever-docnli-ling-2c'\n",
    "classifier = pipeline(\"zero-shot-classification\", model=model, device = 0, batch_size = 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "246013f6-c8f8-4d73-b364-d609bf248f5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying Donald Trump...\n",
      "         Target       MCC        F1  Accuracy     n\n",
      "0  Donald Trump  0.148798  0.172138  0.562793  2134\n",
      "        Dataset       MCC        F1  Accuracy     n\n",
      "0  Kawintiranon  0.206120  0.208589  0.660526   380\n",
      "1          mtsd  0.163214  0.183486  0.552764   398\n",
      "2       Titling  0.355376  0.392157  0.807453   161\n",
      "3   newsletters  0.105427  0.105085  0.472000  1000\n",
      "4     constance  0.136594  0.222222  0.785714    98\n",
      "5       semeval  0.210029  0.410256  0.525773    97\n",
      "CPU times: total: 5.11 s\n",
      "Wall time: 5.18 s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_19336\\2599676197.py:71: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. 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      "  df.loc[df['target'] == target, colname] = res\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "zsclassify(classifier, df = test, colname = 'gS', hypotheses = g_dict, hypothesis_template = 'The author of this text {} Trump.')\n",
    "test['gS'] = [1 if num >= .5 else 0 for num in test['gS']]\n",
    "\n",
    "analyze_results(test, 'adjudicated_sup', 'gS', 'target')\n",
    "\n",
    "analyze_results(test, 'adjudicated_sup', 'gS', 'dataset')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5196a57b",
   "metadata": {},
   "source": [
    "# NLI: Alternative Hypotheses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dbfbeeb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instatiate the pipeline\n",
    "classifier = pipeline(\"zero-shot-classification\", model='MoritzLaurer/DeBERTa-v3-large-mnli-fever-anli-ling-wanli', device = 0, batch_size = 64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "01356116-2b4d-41ac-a123-8c743c50f1a7",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying Donald Trump...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_19336\\2599676197.py:71: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. 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explicitly cast to a compatible dtype first.\n",
      "  df.loc[df['target'] == target, colname] = res\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying Donald Trump...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_19336\\2599676197.py:71: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. 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      "  df.loc[df['target'] == target, colname] = res\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying Donald Trump...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_19336\\2599676197.py:71: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. 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explicitly cast to a compatible dtype first.\n",
      "  df.loc[df['target'] == target, colname] = res\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying Donald Trump...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_19336\\2599676197.py:71: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. 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      "  df.loc[df['target'] == target, colname] = res\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying Donald Trump...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_19336\\2599676197.py:71: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. 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      "  df.loc[df['target'] == target, colname] = res\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying Donald Trump...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_19336\\2599676197.py:71: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. 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      "  df.loc[df['target'] == target, colname] = res\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying Donald Trump...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_19336\\2599676197.py:71: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. 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incompatible with int64, please explicitly cast to a compatible dtype first.\n",
      "  df.loc[df['target'] == target, colname] = res\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classifying Donald Trump...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mikeb\\AppData\\Local\\Temp\\ipykernel_19336\\2599676197.py:71: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. 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      "  df.loc[df['target'] == target, colname] = res\n"
     ]
    }
   ],
   "source": [
    "author_dict = {'Donald Trump': ['is pro']}\n",
    "zsclassify(classifier, df = test, colname = 'author', hypotheses = author_dict, hypothesis_template = 'The author of this text {} Trump.')\n",
    "\n",
    "author2_dict = {'Donald Trump': ['approves of']}\n",
    "zsclassify(classifier, df = test, colname = 'author2', hypotheses = author2_dict, hypothesis_template = 'The author of this text {} Trump.')\n",
    "\n",
    "wrote_dict = {'Donald Trump': ['supports']}\n",
    "zsclassify(classifier, df = test, colname = 'wrote', hypotheses = wrote_dict, hypothesis_template = 'The person who wrote this text {} Trump.')\n",
    "\n",
    "wrote2_dict = {'Donald Trump': ['pro']}\n",
    "zsclassify(classifier, df = test, colname = 'wrote2', hypotheses = wrote2_dict, hypothesis_template = 'The person who wrote this text is {} Trump.')\n",
    "\n",
    "wrote3_dict = {'Donald Trump': ['approves of']}\n",
    "zsclassify(classifier, df = test, colname = 'wrote3', hypotheses = wrote3_dict, hypothesis_template = 'The person who wrote this text {} Trump.')\n",
    "\n",
    "doc_dict = {'Donald Trump': ['supports']}\n",
    "zsclassify(classifier, df = test, colname = 'doc', hypotheses = doc_dict, hypothesis_template = 'The author of this document {} Trump.')\n",
    "\n",
    "doc2_dict = {'Donald Trump': ['is pro']}\n",
    "zsclassify(classifier, df = test, colname = 'doc2', hypotheses = doc2_dict, hypothesis_template = 'The author of this document {} Trump.')\n",
    "\n",
    "doc3_dict = {'Donald Trump': ['approves of']}\n",
    "zsclassify(classifier, df = test, colname = 'doc3', hypotheses = doc3_dict, hypothesis_template = 'The author of this document {} Trump.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ca1dbfc3-e8d0-4c44-bc52-1bf72561398b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['gL', 'author', 'author2', 'wrote', 'wrote2', 'wrote3', 'doc', 'doc2', 'doc3']\n",
    "\n",
    "for col in cols:\n",
    "    test[col] = [1 if num >= .5 else 0 for num in test[col]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a03a9afd-2201-40bc-9589-6940f05d8531",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Target       MCC        F1  Accuracy     n\n",
      "0  Donald Trump  0.653871  0.821668  0.825679  2134\n",
      "         Target      MCC        F1  Accuracy     n\n",
      "0  Donald Trump  0.65876  0.826852  0.824742  2134\n",
      "         Target       MCC        F1  Accuracy     n\n",
      "0  Donald Trump  0.623886  0.790576  0.812559  2134\n",
      "         Target       MCC        F1  Accuracy     n\n",
      "0  Donald Trump  0.642229  0.816714  0.819119  2134\n",
      "         Target       MCC        F1  Accuracy     n\n",
      "0  Donald Trump  0.634029  0.814643  0.812559  2134\n",
      "         Target       MCC        F1  Accuracy     n\n",
      "0  Donald Trump  0.615718  0.793522   0.80881  2134\n",
      "         Target       MCC        F1  Accuracy     n\n",
      "0  Donald Trump  0.648579  0.816446  0.824274  2134\n",
      "         Target       MCC        F1  Accuracy     n\n",
      "0  Donald Trump  0.638934  0.817343  0.814433  2134\n",
      "         Target       MCC        F1  Accuracy     n\n",
      "0  Donald Trump  0.614898  0.783527  0.807873  2134\n"
     ]
    }
   ],
   "source": [
    "for col in cols:\n",
    "    analyze_results(test, 'adjudicated_sup', col, 'target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3aeeaf45-e549-4a16-9d07-ce904f9e3bf2",
   "metadata": {},
   "outputs": [],
   "source": [
    "res = []\n",
    "for col in cols:\n",
    "    res.append(matthews_corrcoef(test['adjudicated_sup'], test[col]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "76661045-14fe-48a5-b93b-5f348962f168",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6587596817350388\n",
      "0.6148975863803131\n",
      "0.6367670253717498\n"
     ]
    }
   ],
   "source": [
    "print(max(res))\n",
    "print(min(res))\n",
    "print(sum(res)/len(res))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "dfc7a687",
   "metadata": {},
   "outputs": [],
   "source": [
    "# export all results for plotting\n",
    "test.to_csv('./trump_test_nli.csv', index = False)"
   ]
  }
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